Adaptive Brain State Neuromodulation for Early Seizure Prevention
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Solution Overview
Problem
Current neuromodulation therapies for drug-resistant epilepsy, such as Responsive Neurostimulation (RNS), are ineffective in halting seizures due to relying on short timescale biomarkers and high-energy stimulation, which may be too late and inefficient.
Innovation Solution
A closed-loop neuromodulation system using a trained patient-specific model that continuously updates to determine brain states and adaptively delivers low-energy stimulation based on long timescale brain activity, employing a multi-dimensional latent space and custom signal processing to predict and prevent seizures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If high-energy stimulation is delivered upon detection of pre-ictal signatures, then seizure progression is attempted to be halted, but the treatment effectiveness is reduced due to late detection and inefficient stimulation
Solution Approach 1:
The system performs preliminary action by continuously monitoring brain conduction data and projecting it through a trained patient-specific model to predict future brain states before seizures occur. The model forecasts long-timescale brain states (hours to days ahead) rather than waiting for short-timescale pre-ictal signatures, enabling proactive intervention before the seizure actually begins.
Solution Approach 2:
The system applies dynamics by continuously adapting and updating the patient-specific model with new conduction data over time. The model evolves to capture changing brain states and seizure patterns specific to each patient, allowing the system to improve its prediction accuracy and timing continuously rather than relying on fixed, pre-programmed detection thresholds.
2Adaptability or versatility
If traditional stagnant biomarkers are used for neuromodulation, then the system is simpler to implement, but the adaptability to individual patient changes is limited
Solution Approach 1:
The system implements feedback by continuously receiving brain conduction data, comparing it against the trained model predictions, and using the results to update both the model and the neuromodulation parameters. This closed-loop feedback enables the system to adapt to individual patient changes in real-time, adjusting treatment parameters based on actual response and evolving predictions.
Solution Approach 2:
The system applies self-service by automatically training and updating the patient-specific model using the patient's own conduction data without requiring manual retraining or external intervention. The model serves itself by continuously learning from new data, automatically adapting to the patient's changing brain patterns and seizure characteristics.
3Measurement precision
If short timescale biomarkers are monitored, then the response time is faster, but the predictive capability for seizure prevention is insufficient
Solution Approach 1:
The system performs preliminary action by using the trained patient-specific model to forecast brain states on long timescales (hours to days) before seizures occur. The model analyzes historical conduction data to identify patterns that predict future seizures, enabling prediction well in advance of the actual seizure event rather than only minutes before.
Solution Approach 2:
The system applies dimensionality change by transforming conduction data into a multi-dimensional latent space representation through the trained model. This allows the system to capture complex, non-linear relationships in the data that are not apparent in traditional single-dimensional biomarker analysis, enabling more accurate long-term predictions.
Data Source
AI summary
A condition of a brain can be treated with closed-loop neuromodulation. At least one recording electrode can record conduction data from at least a portion of the brain. At least one stimulating electrode can apply an electrical signal to another portion of the brain. A controller can execute stored instructions and a stored patient-specific model to: receive the conduction data at a time; project the conduction data through the trained patient-specific model to determine a brain state at the time; update at least one parameter of the electrical signal based on a propensity of the brain state at the time to cause an effect of the conduction of the brain; and update the trained patient specific model to include the brain state at the time and an effect of the updated parameter of the electrical signal on the brain state at the time.